Second Screen User Profiling and Multi-level Smart Recommendations in the context of Social TVs
This work addresses the problem of enhancing user experience in Social TV environments for viewers and content providers, though it appears incremental as it builds on existing collaborative filtering techniques.
The paper tackles the challenge of providing personalized recommendations for Social TV users across first and second screens by developing a context management mechanism that captures social patterns and applies collaborative filtering. The approach was evaluated on a real movie rating dataset, showing effectiveness and efficiency in recommendations.
In the context of Social TV, the increasing popularity of first and second screen users, interacting and posting content online, illustrates new business opportunities and related technical challenges, in order to enrich user experience on such environments. SAM (Socializing Around Media) project uses Social Media-connected infrastructure to deal with the aforementioned challenges, providing intelligent user context management models and mechanisms capturing social patterns, to apply collaborative filtering techniques and personalized recommendations towards this direction. This paper presents the Context Management mechanism of SAM, running in a Social TV environment to provide smart recommendations for first and second screen content. Work presented is evaluated using real movie rating dataset found online, to validate the SAM's approach in terms of effectiveness as well as efficiency.